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While I am not a fan of cookie cutter 'how to' processes, I think that the following steps provide a basic roadmap to thinking about website analytics and to getting started to use them as part of your on-line business.

1. Align what you measure to your business objectives

Think about the alignment of three elements:

Your business goal is something concrete that ties into the purpose of your web presence. It could be something like: 10% of my revenue and profit will come from the website within 18 months.

Site goals are really the first level drill down of the business goal. They could include something like: increase sales in each market by 25%.

The segment based KPIs are the key metrics that will be used to determine how well your business is meeting its site goals. For example: average order size and conversion rate from paid advertising in the USA.

By aligning business objectives, site goals and measuring with KPIs that tie into these goals, you will always have a clear idea of how well your site is performing.

2. Setup your analytics implementation to measure properly

Tag everything on your site. While tagging each page so that it appears in your data seems logical, remember to also tag everything that does not act like a page - file downloads, reader comments, interactions with Flash objects. Remember, your KPIs are going to be affected by everything on your site.

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Tag everything you control coming into your site - your ad campaigns, social media promotion, blogs, email campaigns so that you know where your visitors are coming from, how much they cost, and how well each of them is contributing to your site goals.

Remove the noise. Make sure to filter out any noise from your visitor data like your internal office traffic or your site developer who may be running tests. Keep it clean!

3. Analyze your data in a segmented way

Start off by defining segments - traffic sources are a good starting point as are visitor types such as new visitor and returning visitors. But, go beyond these segments and look for site interactions and behaviors that go beyond the obvious. For example: are visitors who perform certain action on the site such as downloading a brochure a distinct segment with characteristics that differ from other segments visitors.

4. Develop real insight from your analysis

Channel effectiveness – which visitors contribute profitably to site goals and which visitors are not profitable

Content effectiveness – what site content is compelling and contributes to site goals

Process effectiveness – how effective are on site processes in driving site goals

Then... develop hypothesis around what needs to be tested to improve areas that are less effective. If you know that your checkout process has an abandonment rate of 75% at the initial step, perhaps you should ask yourself whether obliging visitors to create an account is really needed or can they just use a guest checkout process.

5. Test, improve and remeasure

Take the hypotheses you developed from your insights and implement them in a test and learn environment and compare your results to original performance. If you have improved, implement the changes on a broader scale.

6. Embed web analytics in your company's website business

Unless web analytics becomes a part of your everyday web business with resources dedicated to the process, you will constantly be playing catchup - never knowing how well your site is performing, why it is performing the way it does or how to improve it.

Google Analytics is a great tool to help you understand where to find value within your website and to help you improve what is not working. The first step to unlock this insight requires that you set up Analytics so that the data you are gathering and analyzing serves your business objectives. The old adage 'garbage in, garbage out' is very fitting.

What to look for in a basic Google Analytics setup

There are a few basic steps you need to take so that the data you are gathering is clean and relevant.

1. Make sure to create one Analytics profile that is unfiltered so that you have a complete history of all activity on your site regardless of source.

2. Create a second profile that filters out traffic that creates business 'noise'. Examples of 'noise' traffic include your internal office visitors as well as consultants and agencies who may be working on your site. Basically, anyone who will be accessing your site but whose visitor traffic tells you nothing about how your on-line business is performing should be excluded from the second profile which becomes the 'business master' profile.

3. Tag everything on your site! You can start by simply tagging all of your pages. BUT... tracking pages alone tells a partial story, particularly when there are other interactions which do not generate 'pageviews'. Think about someone downloading a brochure/newsletter/sample in pdf format. Think about a visitor interacting with your Flash movie or embedded YouTube video. These are likely to be interactive events without pageviews and are therefore not tracked even if you are tagging your pages. So, tag your events! Analytics provides great event tracking reporting that can be used to segment your visitors you never thought about. Ask yourself - are the visitors who download my brochure more likely to purchase a product? With event tracking you can begin to answer this question.

4. Tag all the inbound links that you control - the Tweets, Facebook pages and posts, and LinkedIn updates that you create; any paid advertising that comes from sources other than Google Adwords. Anything that links into your site that you control can be tagged and tracked. So start tagging!

5. If you are running Google Adwords, make sure to link Adwords to Analytics and to turn on auto-tagging in Adwords. Otherwise you will likely see all of your paid search as organic traffic. And beware - if you are managing Ad campaigns for different websites from a single Adwords account, then you need to set up filters in your business master profile that removes the campaign data that belongs to these other sites. Otherwise it will appear as if you are attributing all of the Adwords account traffic, costs and conversions to your single site.

6. If you are running an Ecommerce site make sure to add the Google Analytics transaction tracking code to the page that indicates that someone concluded a transaction - like your Thank You page. That way you are able to capture detailed data about each transaction.

7. Last but not least - set up at least one goal in your business master profile. The goal(s) should relate back to your business and site objectives. If you cannot think of a goal, then ask yourself a simple question - what am I trying to achieve with this site - Sell stuff? Answer customer inquiries? Source leads? Then set up your goals .

As someone who has been analyzing on-site web data for a number of years, I have observed a phenomenon that for the lack of a better term, I call the 'fuzzy factor'. It refers to the differences observed in conversions reported by the analytics tool (in my case I have been using Google Analytics) from paid advertising and the conversions reported by the advertising platforms (Adwords, Yahoo, Bing, Facebook). Note that when I refer to the term 'conversion' in this context, I am referring specifically to a conversion that is synonymous with a 'purchase' transaction and not to any other conversions that I may be tracking such as a newsletter signup.

My process is as follows:

When I create an ad on any third party ad delivery system (like Bing, Yahoo, Facebook) I ensure that the ads are tagged accurately. Since I use Google Analytics I encode the destination URLs using their utm_campaign, utm_medium, utm_source, utm_term and utm_content parameters for every ad that is run. I use Google's autotagging feature so that Google does the work for me for all the Adwords ads.

I install the conversion tracking code supplied by the ad platform on the conversion page and then let the data flow.

I then compare the transactions attributed to the various ad platforms by Analytics to the attributions made by the individual ad platforms and measure the differences.

I know there are a number of reasons for the differences, a major one being that Google Analytics uses a 'last' clicked source to attribute the source of the conversion while the ad platforms (including Adwords) begin tracking a cookie from the time the ad is first clicked and attribute a conversion to that click provided the cookie has not expired.

So, if someone comes to a site by clicking on an Adword ad, leaves without making a purchase, returns the following day by virtue of a Google organic search on the brand name and makes a purchase, Google Analytics will attribute the conversion to Google organic search while Adwords will record a conversion that is attributed to the ad that was first clicked. For GA the score is Organic 1 CPC 0 while the score for Adwords is CPC 1 Organic 0.

My analysis has shown a consistent discrepancy as I look at data going back about 2 years. (Caveat: Facebook only began conversion tracking on their ad platform in early Feb 2010 so my Facebook experience dates back to late Feb 2010 when most of the bugs were ironed out).

In the case of my personalized book sites, Alphakid, Printakid and Livrepersonnalise, this discrepancy is about 15%. For these sites, the advertising platforms are attributing 15% more conversions to the ads than Google Analytics is attributing to them and the overstatement percentages are consistent from ad platform to ad platform.

This is actually not surprising since not everyone who makes a purchase on the sites does so during their first visit. It may take a couple of visits with subsequent visits more likely to originate from non-paid sources.

So what does this mean for me? Here are some of the implications:

Calculating advertising ROI based solely on Analytics conversions will understate the value that the advertising brings to the business.

The initial ad that drives someone to a site is an important source of value and some of the value is hidden. Since not everyone's first purchase coincides with their first site visit, the purchase may never happen without that initial click on the ad. (I can also confirm this via GA's stats on days to purchase and visits to purchase)

I then re-adjust the calculation for lifetime customer value rather than one off purchases. While this adjustment cannot be precise (do you know how many purchases someone is likely to make if your site is new, can you estimate how ad costs might change over time, how your product mix may change thereby affecting average purchase value and your cost of goods) at least it will provide some insight. As a proxy you can use Google's new custom variables to set up visitor based segments for your ad channels and measure lifetime value over a two year period (the 2 years before the cookie expires).

While this might sound a bit complicated, the results are certainly worth the effort, particularly if your web business includes a significant paid ad component.

Has anyone else had similar experiences comparing the data from their ad platforms with the data from their analytics tools?

I met with a former colleague the other day who had recently accepted a new web marketing / communications position with a large brick and mortar retailer. As part of her new responsibilities she needed to familiarize herself with the company's website and asked whether I might be able to help her think through an approach she could take to make sense of the company's web analytics data. While she had a sense of the potential of a tool like Google Analytics, she was less familiar with how to use the tool to generate actionable insight.

Think business and set yourself up to succeed

Rather than starting with a deep dive of every menu, submenu and report available in Google Analytics, we began with a discussion of business objectives, site goals and key measures, organizational responsibilities, and the decision making process vis-à-vis the web site. I hoped that this would provide insight into ‘where’ to begin the data exploration process rather than just ‘boiling the data ocean’.

In my colleague’s case, business objectives were unclear – a site that served partly as a corporate brochure, partly as an additional platform to distribute an on-line version of their print flyers (their primary marketing vehicle is via print advertising distributed to households) and partly as a source of content in subject areas adjacent to the products they sell. The problem, we discovered together when looking at the site and the analytics data, was that for these objectives there were no goals being measured and that what was being measured did not align with their objectives.

When we discussed who was responsible for prioritizing what was posted to the site, the answer was clear – it was a marketing responsibility. But while the marketing department was responsible for site content, they had no involvement in the web analytics process, nor did they look at any web analytics data, nor even generate basic reports from the analytics solution. So how did they make decisions about their site? The words ‘it looks good in the browser’ seemed to convey the decision making process. In fact, web analytics is a foreign concept to the organization.

The conclusion that my colleague and I drew was that currently, she had no immediate way to tell whether or not the site was ‘successful’ and that a process to fill the gaps both in the data and within the organization needed to be initiated. At least now she had a starting point to turn the process around.

The next time you look at your website, ask yourself if what you are measuring helps you reach your goal of what constitutes a successful site.

If you can’t put a measure to success, how do you know when you get there?